A predictive nomogram model for overall survival in obstructive colorectal cancer based on clinical and laboratory indicators

Introduction

Colorectal cancer (CRC) ranks as the third most commonly diagnosed malignancy and the second leading cause of cancer-related mortality worldwide (1). In 2022, China reported approximately 517,100 new CRC cases, accounting for 10.7% of all malignant tumors diagnosed that year (2). A severe and life-threatening complication of CRC is intestinal obstruction, which is frequently encountered in clinical emergency settings and associated with a high mortality rate (3).

Notably, nearly 20% of CRC patients present with intestinal obstruction at initial diagnosis (4). Compared to non-obstructive CRC cases, patients with obstruction typically exhibit more advanced disease stages, lower tumor differentiation postoperatively, and a significantly higher risk of distant metastasis. Consequently, their 5-year overall survival (OS) rate ranges from only 31% to 42% (5-7).

Nomograms, as visual tools derived from multivariate regression models such as logistic or Cox regression, have been increasingly employed in oncology to support individualized prognostication and clinical decision-making (8,9). These models translate complex statistical data into an accessible graphical interface, allowing for intuitive estimation of outcome probabilities based on multiple clinical variables (10,11). Compared with traditional prognostic scoring systems, nomograms offer improved accuracy and usability by simultaneously incorporating and visualizing multiple independent predictors. For example, a study by Lv et al. developed a nomogram based on established clinical factors to predict survival in stage IV CRC patients with distant metastases, providing a useful tool for guiding therapeutic strategies (12).

Emerging evidence suggests that the host immune system plays a dual role in tumor development, contributing to both tumor suppression and promotion (13-16). Inflammatory responses, reflected in alterations of hematologic biomarkers, may hold prognostic significance in malignancies including CRC (17). Several inflammation-based indices such as the neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), lymphocyte-to-monocyte ratio (LMR), and systemic inflammatory response index (SIRI) have been independently associated with CRC prognosis (19,20). However, prior studies have largely examined these markers in isolation, without evaluating their combined prognostic utility. Furthermore, the prognostic relevance of inflammation-based biomarkers in patients with obstructive CRC remains underexplored.

Given these gaps, the present study aims to comprehensively investigate the prognostic value of preoperative blood-based inflammatory and nutritional markers in patients with obstructive colorectal cancer (oCRC). Furthermore, we sought to develop and validate a nomogram model integrating these parameters to improve individualized survival prediction and inform clinical decision-making. We present this article in accordance with the TRIPOD reporting checklist (available at https://jgo.amegroups.com/article/view/10.21037/jgo-2026-0529/rc).

Methods

Study population

This retrospective study included 167 patients diagnosed with oCRC who were admitted to the hospital between February 2019 and February 2021 (Figure 1). To develop and internally validate a prognostic model, patients were randomly assigned to a training cohort (n=116) and a validation cohort (n=51) at a 7:3 ratio. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the Institutional Review Board of Fujian Medical University Union Hospital (No. 2024KJT060). Written informed consent was obtained from all individual participants prior to their inclusion in the study.

Figure 1 Flowchart of patient enrollment. CRC, colorectal cancer; oCRC, obstructive CRC.

According to the 2016 Chinese Consensus on Diagnosis and Treatment of Malignant Intestinal Obstruction and the 2021 European Society for Medical Oncology (ESMO) Guidelines, the clinical diagnostic criteria for oCRC include: (I) a clear history of malignant tumors; (II) typical symptoms of intestinal obstruction, such as abdominal pain, bloating, vomiting, and cessation of bowel movements; (III) physical examination shows hyperactivity of intestinal sounds or increased respiratory and water sounds, accompanied by abdominal distension. The imaging diagnostic criteria are as follows: all patients must undergo abdominal CT examination, and the diagnosis of obstruction must meet at least two criteria: (I) proximal intestinal dilation (small intestine diameter >3 cm or colon diameter >6 cm); (II) remote intestinal collapse; (III) the boundary of the transition zone is clear; (IV) there exists a gas-liquid plane. Exclusion criteria included: mechanical obstruction caused by non-tumor factors such as postoperative adhesions and radiation-induced stenosis; pregnancy; psychiatric disorders; hematologic diseases; chronic liver disease; chronic kidney disease; autoimmune disorders; long-term corticosteroid therapy; co-existing infectious diseases involving other organ systems; recurrent CRC or multiple primary CRCs; history of other malignancies; inoperability due to advanced tumor stage or severe cardiopulmonary dysfunction; and incomplete clinical data.

Data collection

Based on these raw laboratory values, the following inflammation- and nutrition-related composite indices were calculated: NLR, LMR, PLR, monocyte-to-lymphocyte ratio (MLR), SIRI, and albumin-to-globulin ratio (AGR).

Statistical analysis

All statistical analyses were performed using SPSS software version 27.0 (IBM Corp., Armonk, NY, USA) and R software version 4.4.1 (R Foundation for Statistical Computing, Vienna, Austria). Optimal cutoff values for continuous variables were determined using receiver operating characteristic (ROC) curve analysis, with thresholds selected based on the point maximizing the Youden index.

To assess multicollinearity among candidate variables, the variance inflation factor (VIF) was calculated. Variables exhibiting significant collinearity (VIF >5) were excluded from subsequent analyses. Patients were randomly assigned to either the training cohort (n=116) or the validation cohort (n=51) in a 7:3 ratio. Baseline characteristics between the two cohorts were compared using the chi-square test for categorical variables.

Univariate Cox proportional hazards regression analyses were conducted to identify potential prognostic factors associated with OS. Variables with a P-value<0.05 in univariate analysis were subsequently included in the multivariate Cox regression model. Independent prognostic factors were identified based on multivariate analysis results (P<0.05). Model variable selection was performed using backward stepwise regression guided by the Akaike information criterion (AIC) to optimize model fit.

A prognostic nomogram was then constructed based on the final multivariate Cox model using the “rms” package in R. The predictive performance of the model was assessed in both the training and validation cohorts by evaluating discrimination, calibration, and clinical utility. Discrimination was measured using time-dependent ROC curves generated with the “pROC” package. Calibration was assessed using calibration plots created with the “rms” package, comparing predicted and observed survival probabilities. Clinical utility was further evaluated via decision curve analysis (DCA) using the “rmda” package in R, which quantified the net benefit across a range of threshold probabilities. Internal bootstrap validation (200 resamples with replacement) was performed on the training cohort to correct for overfitting optimism and assess model stability. The bias-corrected concordance index (C-index) with standard deviation and 95% bootstrap percentile confidence interval was calculated.

Results

Patient characteristics and Cox regression analysis

Baseline characteristics of patients in the training and validation cohorts were compared using the chi-square test to assess distributional balance. Variables analyzed included demographic data (sex and age), clinical features (stent placement, chemotherapy, tumor size, site of obstruction, type of surgery), tumor staging [T, N, M, and overall tumor-node-metastasis (TNM) stage], histologic grade, obstruction type, and laboratory parameters [white blood cell (WBC), monocyte (Mono), platelet (PLT), mean platelet volume (MPV), total protein (TP), albumin (ALB), carcinoembryonic antigen (CEA), CA19-9, LMR, SIRI, PLR, prognostic nutritional index (PNI), and AGR]. Among these, no statistically significant differences were observed between cohorts for most variables (P>0.05), indicating comparability between the two groups. However, a significant difference was identified in AGR (P=0.04) and MLR (P=0.03), suggesting mild imbalance in these parameters. Despite this, the overall similarity across variables supports the assumption that the training and validation cohorts are derived from the same underlying population, ensuring generalizability of the predictive model (Table 1).

During follow-up, 82 of 167 patients died, corresponding to an overall event rate of 49.1%. There were 58 deaths among 116 patients in the training cohort and 24 deaths among 51 patients in the validation cohort. The median follow-up duration, was 23.2 months (11.65, 56.15) for the entire cohort, 25.9 months (13, 56.85) for the training cohort, and 20.5 months (7.4, 38.4) for the validation cohort. To identify prognostic indicators for OS, univariate Cox proportional hazards regression analysis was performed on the training cohort. Of the 27 clinical and laboratory variables evaluated, 10 were significantly associated with OS (P<0.05): T stage, N stage, M stage, TNM stage, obstruction type, tumor grade, PLT, CA19-9, AGR, and PLR (Table 2). Specific hazard ratios (HRs) and 95% confidence intervals (CIs) were as follows: T stage: HR =2.62 (95% CI: 1.55–4.42), P<0.001; N stage: HR =1.97 (1.10–3.40), P<0.001; M stage: HR =3.13 (1.84–5.33), P<0.001; TNM stage: HR =0.48 (0.36–0.62), P<0.001; Grade: HR =2.13 (1.18–3.76), P<0.001; Obstruction type: HR =0.52 (0.28–0.99), P=0.045; PLT: HR =1.77 (1.03–3.03), P=0.04; CA19-9: HR =3.59 (2.03–6.32), P<0.001; AGR: HR =1.79 (1.04–3.08), P=0.04; PLR: HR =1.88 (1.11–3.20), P=0.02. Kaplan-Meier survival curves were generated to visualize the survival differences associated with each prognostic variable (Figure 2). The divergence between curves reflects the magnitude of prognostic impact, with greater separation indicating more significant differences in survival outcomes.

Figure 2 Kaplan-Meier curves of OS for patients with oCRC in the training cohort. (A) T stage, (B) N stage, (C) M stage, (D) TNM stage, (E) grade, (F) obstruction type, (G) PLT, (H) CA19-9, (I) AGR and (J) PLR. AGR, albumin-to-globulin ratio; CA19-9, carbohydrate antigen 19-9; M stage, metastasis stage; N stage, node stage; oCRC, obstructive colorectal cancer; OS, overall survival; PLR, platelet-to-lymphocyte ratio; PLT, platelet; T stage, tumor stage; TNM stage, tumor-node-metastasis stage.

Subsequently, multivariate Cox regression analysis was conducted using the 10 significant variables from the univariate analysis. Five independent prognostic factors remained statistically significant: M stage: HR =1.917 (1.005–3.657), P=0.048; Tumor grade: HR =0.229 (0.096–0.543), P=0.001; CA19-9: HR =3.919 (2.038–7.538), P<0.001; AGR: HR =2.108 (1.158–3.817), P=0.02; PLR: HR =1.873 (1.013–3.464), P=0.045. These variables were retained in the final prognostic model (Figure 3). Elevated M stage, CA19-9, AGR, and PLR levels, along with poorly differentiated tumor grade, were identified as independent risk factors associated with decreased overall survival in patients with oCRC (P<0.05 for all).

Figure 3 Forest plot of multivariate cox regression analysis. *, P<0.05; **, P<0.01; ***, P<0.001. AGR, albumin-to-globulin ratio; CA19-9, carbohydrate antigen 19-9; CI, confidence interval; M stage, metastasis stage; N stage, node stage; PLR, platelet-to-lymphocyte ratio; PLT, platelet; T stage, tumor stage; TNM stage, tumor-node-metastasis stage.

Nomogram model for predicting prognosis in oCRC

Based on the five independent prognostic factors identified through multivariate Cox regression analysis—M stage, tumor grade, CA19-9 level, AGR, and PLR—a nomogram was constructed to estimate the 1- and 3-year OS probabilities in patients with oCRC (Figure 4). In the nomogram, each prognostic variable was assigned a weighted point value according to its relative contribution to the survival outcome. By summing the individual scores across all variables, a total risk score was calculated for each patient. This total score was then mapped to the corresponding survival probability using the nomogram’s risk scale.

Figure 4 Nomogram for predicting overall survival. AGR, albumin-to-globulin ratio; CA19-9, carbohydrate antigen 19-9; G, grade; M, metastasis; PLR, platelet-to-lymphocyte ratio.

For illustrative purposes, consider a hypothetical patient presenting with the following characteristics: presence of distant metastasis (M1, 66 points), poorly differentiated tumor (Grade III, 100 points), AGR <1.26 (0 points), PLR <245 (0 points), and CA19-9 <37 U/mL (0 points). The cumulative total score for this patient is 166. According to the nomogram, this score corresponds to an estimated 1-year OS probability of approximately 70% and a 3-year OS probability of approximately 28%.

This predictive model enables individualized risk assessment and may facilitate more informed clinical decision-making regarding treatment planning and follow-up strategies for patients with obstructive CRC.

Model performance and validation

The predictive performance of the nomogram model was evaluated through discrimination, calibration, and clinical utility analyses. Discrimination was assessed using the area under the AUC. The model demonstrated good discriminative ability, with an AUC of 0.721 in the training cohort and 0.776 in the validation cohort (Figure 5), indicating acceptable predictive accuracy for OS. To quantitatively evaluate model stability and correct for overfitting optimism, we implemented bootstrap internal validation with 200 resamples on the training cohort. The bias-corrected C-index was 0.739, with a standard deviation of 0.023, and 95% bootstrap CI (0.689–0.773) (Figure S1).

Figure 5 ROC curve of the predictive model. AUC, area under the curve; ROC, receiver operating characteristic.

Calibration was evaluated using calibration plots, which compared the predicted survival probabilities with the actual observed outcomes at 1 and 3 years. The calibration curves for both the training and validation sets showed close alignment with the ideal 45-degree line, indicating high concordance between predicted and observed survival probabilities (Figure 6).

Figure 6 Calibration curves for predicting patient OS at (A) 1-year and (B) 3-year in the training cohort, and (C) 1-year and (D) 3-year in the internal verification. OS, overall survival.

To assess the clinical applicability of the nomogram, DCA was performed. As shown in Figure 7, the nomogram model demonstrated a higher net benefit across a range of threshold probabilities (50–90%) compared with the default strategies of treating all patients or treating none. This indicates that the model provides meaningful clinical utility and may support personalized decision-making in the management of patients with oCRC.

Figure 7 Decision curve analysis of the predictive model.

Discussion

CRC remains one of the leading causes of cancer-related mortality globally. According to the National Comprehensive Cancer Network (NCCN) guidelines, intestinal obstruction is considered a high-risk factor for recurrence and poor outcomes. CRC-associated intestinal obstruction often presents complex clinical challenges and is typically associated with more advanced disease stages, poorer tumor differentiation, and a higher likelihood of metastasis compared to non-obstructive CRC. Despite its clinical significance, prognostic models specifically tailored for oCRC remain inadequately developed.

In the present study, we constructed a nomogram model based on five independent prognostic factors—M stage, tumor differentiation, AGR, PLR, and CA19-9—identified through univariate and multivariate Cox regression analyses. This model was designed to provide individualized estimates of 1- and 3-year overall survival OS in patients with oCRC, which may facilitate more informed clinical decision-making. In the Cox regression model, we use the Youden index for ROC curve analysis to determine the optimal critical value, and perform binary treatment on continuous variables, which can significantly improve the interpretability of the results. In addition, to ensure the robustness of the research conclusions, we also conducted additional Cox regression analysis on these laboratory indicators in the form of continuous variables. As shown in Table S1, the analysis results are consistent with those obtained using binary variables, which further confirms the reliability of our conclusion.

The American Joint Committee on Cancer (AJCC) TNM staging system continues to serve as a cornerstone in prognostic stratification for CRC. In our analysis, distant metastasis (M stage) emerged as a robust and independent predictor of poor survival, in line with existing literature. Although N stage demonstrated significance in univariate analysis, it did not retain its predictive value in multivariate modeling, potentially due to limited sample size or interaction with other variables in the final model.

Tumor differentiation is another critical determinant of CRC prognosis. Poorly differentiated tumors exhibit greater cellular heterogeneity, increased invasiveness, and early metastatic potential. Our findings suggest that lower differentiation grades may be significantly associated with reduced survival and could serve as independent prognostic indicators for oCRC.

The tumor microenvironment—comprising neutrophils, lymphocytes, monocytes, and platelets—plays a pivotal role in cancer progression and host immune responses (21). Several inflammation-based biomarkers, including NLR, LMR, PLR, SIRI, and PNI, have been explored for their prognostic relevance in CRC (22). In our study, PLR was identified as an independent prognostic factor. Elevated PLR may reflect a pro-inflammatory and immunosuppressive milieu that facilitates tumor progression.

Nutritional status, as assessed by serum albumin and globulin levels, also influences cancer outcomes. AGR, a composite marker of nutrition and systemic inflammation, demonstrated significant prognostic value in this cohort. Hypoalbuminemia often indicates malnutrition or systemic inflammation, while elevated globulin levels may reflect chronic immune activation. In this study, higher AGR values (≥1.26) were associated with poorer prognosis, which is contrary to the results of most previous studies (23,24). In patients with end-stage malignant intestinal obstruction, high AGR may reflect a special state of relative retention of albumin but significant decrease in globulin: end-stage cachexia is often accompanied by severe immune suppression, leading to a decrease in globulin synthesis. And the compensatory maintenance of albumin before liver synthesis failure. At this point, high AGR may indicate immune system breakdown rather than good nutrition, and is associated with extremely short survival.

Tumor biomarkers such as CEA, CA19-9, and CA242 are widely utilized in CRC diagnosis and surveillance (25). Elevated preoperative levels of these markers are correlated with tumor aggressiveness and poor prognosis. In this study, CA19-9 ≥37 U/mL emerged as an independent prognostic factor, likely reflecting a higher tumor burden, greater lymph node involvement, and poor differentiation. These findings support the inclusion of CA19-9 in prognostic models for oCRC.

Compared to the traditional TNM staging system, nomogram-based prediction models offer a more comprehensive and individualized assessment by integrating multiple prognostic variables (26-28). Nomograms have been successfully applied across various malignancies—including gastric, colorectal, and prostate cancers—to improve survival prediction and guide personalized treatment strategies (29-32).

Our model demonstrated good discrimination and calibration in both the training and validation cohorts. DCA further suggested its potential clinical utility by showing superior net benefit across a wide range of threshold probabilities. These findings suggest that the model may have potential value in routine clinical practice for stratifying oCRC patients based on mortality risk.

Nonetheless, the study has limitations. This model may serve as an exploratory risk stratification tool, although it shows initial potential, further validation is needed. It is a single-center retrospective analysis, which may introduce selection bias. Additionally, the relatively small sample size and limited follow-up duration may affect the generalizability and robustness of the model. Future studies incorporating larger, multicenter cohorts with extended follow-up are warranted to further validate and refine the predictive accuracy of the proposed nomogram.

Conclusions

This study identified M stage, tumor differentiation, AGR, PLR, and CA19-9 as independent prognostic factors for overall survival in patients with oCRC through univariate and multivariate Cox regression analyses. Based on these variables, a nomogram was developed to provide an individualized and visual tool for survival prediction. The model demonstrated favorable discriminatory ability and calibration in both the training and validation cohorts, as evidenced by ROC and DCA. These findings suggest that the proposed nomogram may serve as a practical and reliable prognostic tool to support clinical decision-making and personalized risk stratification in patients with oCRC.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the TRIPOD reporting checklist. Available at https://jgo.amegroups.com/article/view/10.21037/jgo-2026-0529/rc

Data Sharing Statement: Available at https://jgo.amegroups.com/article/view/10.21037/jgo-2026-0529/dss

Peer Review File: Available at https://jgo.amegroups.com/article/view/10.21037/jgo-2026-0529/prf

Funding: This work was supported by the Joint Funds for the Innovation of Science and Technology, Fujian Province (No. 2025Y9375 to Z.H.) and the Fujian Provincial Natural Science Foundation of China (No. 2024J01596 to P.L.).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://jgo.amegroups.com/article/view/10.21037/jgo-2026-0529/coif). All authors report that this work was supported by the Joint Funds for the Innovation of Science and Technology, Fujian Province (No. 2025Y9375 to Z.H.) and the Fujian Provincial Natural Science Foundation of China (No. 2024J01596 to P.L.). The authors have no other conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the Institutional Review Board of Fujian Medical University Union Hospital (No. 2024KJT060). Written informed consent was obtained from all individual participants prior to their inclusion in the study.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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